强大的非对称异质联合学习与腐败的客户
概括
本研究介绍了强大的非对称异质联合学习 (RAHFL),以解决数据腐败和模式差异的联合学习. 该RAHFL框架增强了模型的稳定性和选择性学习,以提高性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 系统面临着数据腐败和客户端模型异质性等重大挑战.
- 由噪音或压缩引起的数据损坏严重降低了FL系统的性能.
- 现有的FL方法与多样化的客户端模型和不可靠的数据作斗争.
研究的目的:
- 为强大的联合学习开发一个新的框架,解决模型异质性和数据腐败问题.
- 增强当地模型对各种数据腐败模式的弹性和适应性.
- 在协作学习中,减轻来自不太强大的客户的腐败反的影响.
主要方法:
- 引入了一个强大的非对称异质联合学习 (RAHFL) 框架.
- 提出了一种使用混合数据增强进行强大的特征表示的多样性增强监督对比学习技术.
- 设计了一个不对称的异质联合学习策略,使选择性单向学习能够过低质量的信息.
主要成果:
- 拟议的多样性增强监督的对比学习显著提高了模型对数据腐败的弹性.
- 非对称异质联合学习策略有效地防止了来自表现不佳客户的腐败信息的传播.
- 广泛的实验验证了RAHFL框架在具有挑战性的多样化联合学习场景中的有效性和稳定性.
结论:
- 该RAHFL框架提供了一个强大的解决方案,用于与异质和损坏的客户端数据联合学习.
- 对比式学习和不对称的学习策略的结合提高了整体系统的可靠性和性能.
- 这种方法在为现实应用构建可靠的联合学习系统方面取得了重大进展.
相关概念视频
00:59Propagation of Uncertainty from Random Error
621
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
621
01:10Propagation of Uncertainty from Systematic Error
453
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
453
01:27Associative Learning
276
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
276
01:25Multimachine Stability
129
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
129
01:12Observational Learning
118
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
118
01:25Distribution Reliability and Automation
103
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
103